Partial discharge pattern classification using composite versions of probabilistic neural network inference engine

Karthikeyan, B.; Gopal, S.; Venkatesh, S.

doi:10.1016/j.eswa.2007.02.005

Cited by 38 publications

(17 citation statements)

References 11 publications

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“…Therefore, monitoring the aging status of the transformer will be helpful to prevent system failure (Lin, Wu, & Chung, 2008). Since digital PD detection is developed (Ward, 1992) has extremely high diagnosis accuracy for insulating medium (James & Phung, 1995;Tanaka, 1995), it is an efficient way to well identify the aging status of cast-resin transformer for maintenance or replacement to further prevent insulation breakdown (Farag et al, 1999;James & Phung, 1995;Karthikeyan, Gopal, & Venkatesh, 2008;Tanaka, 1995;Yue, Chen, Cheng, Song, & Xie, 2006).…”

Section: Introductionmentioning

confidence: 99%

Artificial identification system for transformer insulation aging

Kuo¹

2010

Expert Systems with Applications

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Section: Introductionmentioning

confidence: 99%

Artificial identification system for transformer insulation aging

Kuo¹

2010

Expert Systems with Applications

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“…Maximum likelihood training was applied here and encouraging recognition probabilities of 99% were recorded for corona, while lower rates recorded for floating and internal discharges. In other research, Karthikeyan et al [18] also applied the PNN to categorize single source PD patterns and a recognition performance of 100% was obtained for some input PD classes, though misclassification still persisted. This indicates that misclassification is still an issue with the BP ANN, where PD faults are misclassified as others and certain techniques to eliminate this issue must be investigated.…”

Section: Relevant Previous Research Work On Artificial Neural Networmentioning

confidence: 99%

“…Recently, attention has been paid to the application of PNN [18,32], and RBFN [59] to categorize PD fault geometries, i.e., corona and surface discharges in air and oil. Evagorou et al [60] applied the PNN to categorize some PD fault geometries, i.e., corona and surface PD in air and oil.…”

Section: Relevant Previous Research Work On Artificial Neural Networmentioning

confidence: 99%

“…The PNN obtains the probability density function (PDF) based on Bayes' decision making approach [18,28]. The PNN is made up of input layer, hidden layer and output layer [27].…”

Section: The Probabilistic Neural Networkmentioning

confidence: 99%

“…The PNN is a technique based on competitive learning procedure based on the Parzen window concept of multivariate probability approximation [18] ( Figure 5). The PNN obtains the probability density function (PDF) based on Bayes' decision making approach [18,28].…”

Section: The Probabilistic Neural Networkmentioning

confidence: 99%

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Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions

et al. 2016

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Abstract:In order to investigate how artificial neural networks (ANNs) have been applied for partial discharge (PD) pattern recognition, this paper reviews recent progress made on ANN development for PD classification by a literature survey. Contributions from several authors have been presented and discussed. High recognition rate has been recorded for several PD faults, but there are still many factors that hinder correct recognition of PD by the ANN, such as high-amplitude noise or wide spectral content typical from industrial environments, trial and error approaches in determining an optimum ANN, multiple PD sources acting simultaneously, lack of comprehensive and up to date databank of PD faults, and the appropriate selection of the characteristics that allow a correct recognition of the type of source which are currently being addressed by researchers. Several suggestions for improvement are proposed by the authors include: (1) determining the optimum weights in training the ANN; (2) using PD data captured over long stressing period in training the ANN; (3) ANN recognizing different PD degradation levels; (4) using the same resolution sizes of the PD patterns when training and testing the ANN with different PD dataset; (5) understanding the characteristics of multiple concurrent PD faults and effectively recognizing them; and (6) developing techniques in order to shorten the training time for the ANN as applied for PD recognition Finally, this paper critically assesses the suitability of ANNs for both online and offline PD detections outlining the advantages to the practitioners in the field. It is possible for the ANNs to determine the stage of degradation of the PD, thereby giving an indication of the seriousness of the fault.

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Noise Rejection Methods and Data Interpretation

2022

Electromagnetic Analysis and Condition Monitoring of Synchronous Generators

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Partial discharge pattern classification using composite versions of probabilistic neural network inference engine

Cited by 38 publications

References 11 publications

Artificial identification system for transformer insulation aging

Artificial identification system for transformer insulation aging

Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions

Noise Rejection Methods and Data Interpretation

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